Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers Ghanooni, P, Habibi, H, Yazdani, A, Wang, H, MahmoudZadeh, Somaiyeh and Mahmoudi, A 2021, Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers, Electronics, vol. 10, no. 21, pp. 1-17, doi: 10.3390/electronics10212637. Attached Files Name Description MIMEType Size Downloads Title Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers Author(s) Ghanooni, P Habibi, H Yazdani, A Wang, H MahmoudZadeh, Somaiyeh orcid.org/0000-0002-9733-1732 Mahmoudi, A Journal name Electronics Volume number 10 Issue number 21 Article ID 2637 Start page 1 End page 17 Total pages 17 Publisher MDPI Place of publication Basel, Switzerland Publication date 2021 ISSN 2079-9292 2079-9292 Summary This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault-and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs. Language eng DOI 10.3390/electronics10212637 Field of Research 0906 Electrical and Electronic Engineering HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30158348 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 11 Nov 2021, 08:02:24 EST Thu, 11 Nov 2021, 09:08:19 EST Thu, 11 Nov 2021, 09:08:50 EST Thu, 11 Nov 2021, 09:13:37 EST Fri, 12 Nov 2021, 14:43:53 EST Fri, 12 Nov 2021, 15:39:11 EST Fri, 12 Nov 2021, 15:41:00 EST Sat, 11 Dec 2021, 01:31:31 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 48 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 11 Nov 2021, 08:02:24 EST